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Closed 9 years ago.
I'm using the convex optimisation toolbox in matlab and the objective function gets minimized and the value is shown. But the optimisation variable for which minima is achieved, I cannot find. Will someone tell me how to find it?
You didn't say which function you are using to perform the optimization, but most or all of them return the location of the minimum as the first argument. See fmincon for example.
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Closed 10 years ago.
I am new to Matlab and I want to write a matlab program for global optimization. I have an objective function f(x), a domain D, and co-domain R. I have looked at Optimization toolbox, but it show reference and examples of local minimization only.
If someone can give me a few hints on global optimization in Matlab, that will be very helpful.
Optimization Toolbox, as you've discovered, only handles "local" optimization - algorithms such as linear, quadratic and binary programming, nonlinear optimization etc. You might like to take a look at Global Optimization Toolbox, which contains global optimization algorithms such as genetic algorithms, simulated annealing and others..
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Closed 10 years ago.
I have a vector of values corresponding to the measured data and I want to predict the next values. How can I do that? I know that is possible with Kalman filter but it might be an easier way to do. Here is a plot of the data and I want to predict next values:
Try exponential smoothing, e.g., double exponential smoothing or Holt-Winters method. Basically you try to learn the trend of the data.
I have some sample python code in this post.
On the other hand, if you know the movement/observation model of the underline variable, for sure, kalman will give you much better predictions as #tomasz74 pointed out.
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Closed 10 years ago.
Could anyone help with a function that returns the condition number of a matrix based on the euclidean norm?
This is a good explanation about condition number and here is how you do it in Matlab.
c = cond(X)
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Closed 10 years ago.
I'm trying to find effective binarization techniques for document images. I've currently implemented the niblack and sauvola thresholding algorithms and tried binarization based on histogram evaluation as well. Could someone please suggest other binarization methods that have proved to be effective?
Here's a sample degraded image I've been working with:
http://spie.org/Images/Graphics/Newsroom/Imported/0681/0681_fig1.jpg
Any suggestions will be much appreciated.
How about starting with simply adapting the threshold based on the local neighborhood?
im = rgb2gray(im);
im = im2double(im);
f_makebw = #(I) im2bw(I.data, double(median(I.data(:)))/1.45);
bw = ~blockproc(im, [128 128], f_makebw);
Result:
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Closed 11 years ago.
I have a random number generator function. I want to use this function to generate 100 samples for simulation and make a histogram of it. Can anybody tell me how I can do in Matlab?
This random number generator is from a distribution.
The simplest way is just to call it in a loop:
x = zeros(1,100);
for i = 1:100
x(i) = my_func();
end
And then:
hist(x)